modify api detect_overflow name in TrainOneStepWithLossScaleCell

mindspore/nn/wrap/loss_scale.py

@@ -298,7 +298,7 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         loss = self.network(*inputs)
         scaling_sens = self.scale_sense
 
-        status, scaling_sens = self.start_overflow(loss, scaling_sens)
+        status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
 
         scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss))
         grads = self.grad(self.network, weights)(*inputs, scaling_sens_filled)
@@ -307,7 +307,7 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         grads = self.grad_reducer(grads)
 
         # get the overflow buffer
-        cond = self.detect_overflow(status, grads)
+        cond = self.get_overflow_status(status, grads)
         overflow = self.process_loss_scale(cond)
         # if there is no overflow, do optimize
         if not overflow:
@@ -322,7 +322,7 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         else:
             raise TypeError("The input type must be Tensor, but got {}".format(type(sens)))
 
-    def start_overflow(self, pre_cond, compute_input):
+    def start_overflow_check(self, pre_cond, compute_input):
         """
         Start floating-point overflow detection. Create and clear the overflow detection state.
 
@@ -355,9 +355,9 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
             compute_input = F.depend(compute_input, clear_status)
         return status, compute_input
 
-    def detect_overflow(self, status, compute_output):
+    def get_overflow_status(self, status, compute_output):
         """
-        Detect floating-point overflow status.
+        Get floating-point overflow status.
 
         Get overflow results after executing the target process for overflow detection.
 

model_zoo/official/nlp/bert/src/bert_for_pre_training.py

@@ -378,7 +378,7 @@ class BertTrainOneStepWithLossScaleCell(nn.TrainOneStepWithLossScaleCell):
             scaling_sens = self.loss_scale
         else:
             scaling_sens = sens
-        status, scaling_sens = self.start_overflow(loss, scaling_sens)
+        status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
         grads = self.grad(self.network, weights)(input_ids,
                                                  input_mask,
                                                  token_type_id,
@@ -393,7 +393,7 @@ class BertTrainOneStepWithLossScaleCell(nn.TrainOneStepWithLossScaleCell):
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
         grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
 
-        cond = self.detect_overflow(status, grads)
+        cond = self.get_overflow_status(status, grads)
         overflow = cond
         if sens is None:
             overflow = self.loss_scaling_manager(self.loss_scale, cond)
@@ -454,7 +454,7 @@ class BertTrainOneStepWithLossScaleCellForAdam(nn.TrainOneStepWithLossScaleCell)
         else:
             scaling_sens = sens
 
-        status, scaling_sens = self.start_overflow(loss, scaling_sens)
+        status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
         grads = self.grad(self.network, weights)(input_ids,
                                                  input_mask,
                                                  token_type_id,
@@ -468,7 +468,7 @@ class BertTrainOneStepWithLossScaleCellForAdam(nn.TrainOneStepWithLossScaleCell)
         grads = self.grad_reducer(grads)
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
         grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
-        cond = self.detect_overflow(status, grads)
+        cond = self.get_overflow_status(status, grads)
         overflow = cond
         if self.loss_scaling_manager is not None:
             overflow = self.loss_scaling_manager(scaling_sens, cond)